ES2400022T3 - Procedure for risk stratification in stable coronary artery disease - Google Patents

Abstract

In vitro procedure for risk stratification of patients with stable coronary artery disease, in which the concentration of procalcitonin in the circulation of such patients is determined using a highly sensitive procalcitonin assay, and in which within the range of procalcitonin concentrations in In the normal range typical of healthy individuals, cut-off values are defined that distinguish groups of individual patients with stable coronary artery disease according to their personal cardiac risk, and patients are assigned to one of these risk groups based on their individual concentrations of procalcitonin and wherein said procalcitonin concentrations in the typical normal range of healthy individuals are procalcitonin concentrations of 0.1 ng / ml procalcitonin or less if measured with a sensitive procalcitonin assay as described in the present application.

Description

Procedure for risk stratification in stable coronary artery disease.

The present invention discloses a new method for stratification of patients with stable coronary disease (coronary heart disease: CAD) according to the individual cardiac risks of the patients. Patients with stable CAD status are usually patients with CAD demonstrated by angiography, that is, with affected coronary arteries, with plaques on the internal walls of the coronary artery (atherosclerosis) and stenosis in a main coronary artery. CAD is considered a serious cardiac risk. Patients with CAD are considered "stable" if CAD does not manifest itself in the form of acute cardiovascular events.

In view of the immanent risk of future cardiovascular events, it would be highly desirable to be able to distinguish within the group of patients with stable CAD between different groups according to their personal cardiac risk.

so that an “individual alert status” can be determined for a particular patient according to the risk group to which it has been assigned. Such a grouping of patients is usually called "stratification."

The distinction between high-risk patients and patients at moderate or low risk would allow a better selection of the most appropriate therapeutic strategy for a particular patient, avoiding, for example, underestimation of cardiac risk and insufficient medication of high-risk patients , on the one hand, and unnecessary therapeutic interventions, and the associated costs, with low-risk patients, on the other.

In Ilhan et al. (2005), vol. 60, No. 4, p. 361-365, procalcitonin levels are measured in patients with coronary atherosclerosis but, in this case, patients presenting with acute coronary syndrome were compared with patients presenting with stable angina pectoris.

Therefore, it is an objective of the present invention to provide a new method by which patients with stable CAD can be stratified according to their personal cardiac risks, that is to say with respect to their individual risks concerning the future incidence of cardiovascular events.

As explained further in detail below, a study has been conducted to evaluate the possible utility of several analytes (biomolecules, biomarkers) that can be determined in the circulation of patients for a stratification of patients with stable CAD.

In the course of this study, it has been surprisingly discovered that a highly sensitive measurement of the concentration of procalcitonin peptide (PCT) in the circulation of patients with CAD in the range of very low physiological concentrations, concentrations that until now were considered to be Below the diagnostic significance and, therefore, typical concentrations for normal healthy individuals, it allows a useful stratification of patients with CAD, and that the usefulness of a highly sensitive PCT determination of this type can be increased even if the results obtained for PCT in combination with the results of the measurement of the action of an analyte of another type (a vasoactive analyte), exemplified by the so-called type B natriuretic peptide (or cerebral) BNP.

The invention in its broadest sense is defined by independent claim 1:

In vitro procedure for risk stratification of patients with stable coronary artery disease, in which the concentration of procalcitonin in the circulation of such patients is determined using a highly sensitive procalcitonin assay, and in which within the range of procalcitonin concentrations in The typical normal range of healthy individuals defines cut-off values that distinguish groups of individual patients with stable coronary artery disease according to their personal cardiac risk, and patients are assigned to one of these risk groups based on their individual procalcitonin concentrations. and wherein said procalcitonin concentrations in the typical normal range of healthy individuals are procalcitonin concentrations of 0.1 ng / ml procalcitonin or less if measured with a sensitive procalcitonin assay as described in the present application.

Preferred embodiments are defined in claims 2 to 6:

2.

 Method according to claim 1, wherein patients are stratified with respect to their risks regarding the future incidence of cardiovascular events.

3.

 Method according to claims 1 to 2, wherein the patients for whom procalcitonin concentrations of 0.05 ng / ml or more are considered as the patients with the highest cardiac risk.

Four.

 Method according to any one of claims 1 to 3, wherein together with the determination of procalcitonin at least one additional analyte useful for cardiovascular prognosis is determined, said at least one analyte being selected from the group consisting of natriuretic peptides, adrenomedulin endothelins

vasopressin, CRP, neopterin, myeloperoxidase, troponin, GDF-15, cystatin-C and its precursors, their prohormones and associated prohormone fragments.

5.

 Method according to claim 4, wherein said additional analyte comprises BNP, proBNP and associated proBNP fragments.

6.

 Use of the highly sensitive determination of procalcitonin in the circulation of patients at risk of presenting cardiovascular events for prognostic purposes in a procedure according to claims 1 to 5.

Procalcitonin (PCT), which is to be measured according to the present invention, has become a well established biomarker for the diagnosis of sepsis: PCT reflects the severity of the bacterial infection and is used in particular to monitor the progression of infection in septicemia, severe septicemia or septic shock. It is possible to use PCT to measure the activity of the systemic inflammatory response, to monitor the success of the therapy, and to estimate the prognosis (1) (2) (3) (4) (5). The increase in PCT levels in patients with sepsis is correlated with mortality (6).

While a growing number of studies investigate the possible role of PCT in other infectious diseases such as pneumonia, bacterial meningitis and malaria (7) (8) (9), no study has yet reported on the possible use of PCT in risk stratification of patients with stable coronary artery disease (CAD). In vitro studies showed that PCT plays an important role during monocyte migration and adhesion and also has an effect on gene expression of inducible nitric oxide synthase (iNOS) (10) (11) (12). The association between PCT levels and low-grade inflammation of the arterial wall in atherosclerosis and the possible effect on endothelial dysfunction has not been analyzed. This prospective study analyzed the prognosis impact of PCT in a large group of patients with stable angina consecutively included on the cardiovascular outcome to assess the possible clinical applicability of PCT measurements in CAD.

In the context of septicemia and related conditions, in which PCT concentrations reach quite high physiological concentrations, PCT has traditionally been measured by means of a sandwich-type assay using two monoclonal antibodies that bind to different parts of the molecule. PCT so that essentially only the entire PCT molecule is detected (see, for example, (1)). Test sensitivity Typical functional (FAS) of the typical bilateral chemiluminescence assay for PCT is 300 ng / l (0.3 ng / ml or 0.3 g / l). More recently, new highly sensitive assays have been developed for the determination of PCT (28). The functional test sensitivity (FAS, CV between trials <20%) of this new test was <7 ng / l of PCT. Using this assay, typical PCT concentrations could be determined in healthy individuals. In 500 healthy individuals, the range was <7 to 63 ng / l (<0.007 to 0.063 ng / ml), that is, a concentration range well below 0.1 ng / ml. The median determined was 13.5 ng / l (95% confidence interval for the average of 12.6 to 14.7 ng / l).

In a further improved form, said sensitive PCT assay is available as PCT sensitive LIA.

(B.R.A.H.M.S AG, Hennigsdorf, Germany) that has an analytical test sensitivity of 0.01 ng / ml and a functional test sensitivity (FAS) of at least 0.05 ng / ml. A related trial for amplified crypto data emission technology with temporal resolution (TRACE) (Kryptor PCT, B.R.A.H.M.S AG, Hennigsdorf) it has a functional test sensitivity of 0.06 g / l (0.06 ng / ml).

The most recent sensitive PCT trials have been predominantly used in relation to the orientation of antibiotic therapy in lower respiratory tract infections (outpatient pneumonia, NEH; worsening of chronic obstructive pulmonary disease, COPD: see (29), ( 30), (31)). In the case of NEH, antibiotic treatment is recommended based on the PCT concentrations measured as follows: totally advises, greater than 0.5 g / l; it is advised, greater than 0.25 g / l; it is not recommended, less than 0.25 mg / l; it is strongly discouraged, less than 0.1 g / l (29). In other words, concentrations of 0.1 0.1 ng / ml) are considered as typical concentrations for healthy individuals.

The risk stratification procedure of patients with stable coronary artery disease (stable CAD) is based on a differential evaluation of the measured PCT concentrations that are below the value of 0.1 ng / ml for healthy individuals and that to date have not been They have used for forecasting purposes.

The invention is set forth in greater detail in the following sections and Figures 1 to 3 and Tables 1 to 7 mentioned therein. The tables mentioned are found on separate pages at the end of the description text.

In the figures:

Figure 1: shows Kaplan-Meier survival curves showing cardiovascular events according to procalcitonin quartiles;

Figure 2: shows Kaplan-Meier survival curves showing cardiovascular events according to the procalcitonin cut-off point value of 0.05 ng / ml;

Figure 3: Kaplan-Meier survival curves showing cardiovascular events according to procalcitonin and type B natriuretic peptide in combined analysis:

Procedures

Study population

Between November 1996 and January 2004, 3,326 patients with CAD demonstrated by angiography and at least A 30% stenosis diagnosed in a main coronary artery in the Department of Medicine II of Johannes Gutenberg University in Mainz or the Department of Medicine of the Central Hospital of the German Federal Armed Forces in Koblenz, were included in the AtheroGene registry. Additional details on the concept of the AtheroGene study have been described previously (13).

In the present substudy, the exclusion criteria were clinical signs of acute coronary syndrome (class B or C of the Braunwald classification of unstable angina, myocardial infarction without ST segment elevation and acute ST segment elevation). Patients with coronary bypass surgery or coronary revascularization during the last four weeks were also excluded. Additional reasons for exclusion were hemodynamically significant valvular heart disease, surgery or trauma within the previous month, known cardiomyopathy, obvious carcinoma, chronic inflammatory disease, febrile conditions, or the use of oral anticoagulant therapy within four months. previous weeks

The history of classic risk factors was evaluated as set out below. Patients who received antihypertensive treatment or who had already been confirmed the diagnosis of hypertension (blood pressure greater than 160/90 mmHg) were considered to have hypertension. Hyperlipoproteinemia was diagnosed in patients with Hypolipidemic medication or with a history of cholesterol levels 240 mg per deciliter. Patients were currently classified as smokers, as smokers in the past (if they had quit smoking more than 4 weeks and less than 40 years before), or as patients who have never smoked (if they had never smoked or had quit smoking 40 years before or more). Patients who received dietary treatment or medication for diabetes or whose fasting blood glucose level was greater than 125 mg per deciliter were considered to have diabetes mellitus.

1124 patients were followed for a median period of 3.8 (maximum 6.8) years. The patients either presented themselves at the clinic (78.2%) or were interviewed by telephone by qualified medical personnel. Follow-up information was obtained, including death due to cardiovascular causes (n = 40), death due to causes unrelated to coronary artery disease (n = 30) and non-fatal myocardial infarction (n = 32), from hospital records or The GP. The primary endpoint was nonfatal myocardial infarction and cardiovascular death.

The AtheroGene study was approved by the local ethical committee of the University of Mainz. Participants in the study presented German nationality and, in particular, were inhabitants of the Rhein-Main region. All patients were white. Participation was voluntary, and patients were included after obtaining informed written consent.

Laboratory procedures

Blood samples were taken under normalized conditions before performing coronary angiography. Samples were taken when patients entered the catheterization laboratory after a minimum of 12 hours on an empty stomach. Serum lipid levels were measured immediately. Lipid levels were measured using routine procedures (total cholesterol and triglycerides, Roche Diagnostics GmbH, Mannheim, Germany; high density lipoprotein cholesterol, Rolf Greiner Biochemica, Mannheim, Flacht bei Limburg, Germany; and low density lipoprotein cholesterol calculated according to Friedewald's formula). The ratio LDL- / HDL was calculated by dividing LDL levels among those of HDL.

Plasma and serum samples were centrifuged at 4,000 g for 10 minutes, divided into aliquots, and stored at -80 ° C until analysis. PCT was determined by a highly sensitive immunoluminometric assay (BRAHMS PCT sensitive; BRAHMS AG, Hennigsdorf, Germany; analytical test sensitivity: 0.01 ng / ml; functional test sensitivity (coefficient of variation between 20% assays): 0.05 ng / ml). Data was generated using a batch of chemicals. C-reactive protein (CRP) was analyzed by a latex particle enhanced immunoassay (Roche Diagnostics, Mannheim, Germany; detection range: 0.1 to 20 mg / l; coefficient of variation between assays, 1.0 per percent for values of 15 mg per liter and 6.5 percent for values less than 4 mg per liter). Type B natriuretic peptide (BNP) in plasma was determined using a fluorescence immunoassay (Biosite, San Diego, California, USA; detection range: 5 to 5,000 pg / ml; coefficient of variation between assays close to 10 %; insignificant cross reactivity with other natriuretic peptides). All laboratory measurements were performed blindly without knowledge of the patient's clinical status.

Statistical considerations

The average values were calculated (

 standard deviation) and the proportions of the initial cardiovascular risk factors, clinical variables and biomarkers for study participants according to procalcitonin quartiles. Due to the small range of procalcitonin levels, the quartiles do not comprise the same number of patients. The variables with an asymmetric distribution (| asymmetry |> 1) were presented as medians with the quartiles. The correlation analysis was performed using Spearman's range correlation. In another analysis, the risk ratios for the highest quartile were dichotomized versus the other PCT quartiles according to the classic or medium risk factors of the clinical variables and biomarkers.

The association of the PCT and BNP biomarkers with the primary assessment criteria according to the quartiles in different models was analyzed by Cox regression analysis, adjusting the first model for age and sex and adjusting the second possible confounding factors and the factors of classic risk (age, sex, body mass index, hypertension, diabetes mellitus, smoking status, LDL- / HDL ratio, number of affected vessels, beta-blocker and statin therapy). Graphical representations of cumulative events were estimated according to the PCT concentration quartiles using the Kaplan-Meier procedure and compared using the logarithmic range test. All survival analyzes were performed for the primary assessment criterion of non-fatal myocardial infarction or cardiovascular death. Data from patients who died due to causes unrelated to cardiovascular disease at the time of death were censored.

PCT and BNP were logarithmically transformed to enhance the model fit. To compare the predictive power of these biomarkers, the risk ratios were calculated by increasing a standard deviation in univariate and multivariate analyzes (adjusted for classical risk factors and clinical variables). A retrograde Cox regression approach was adopted for multivariate analyzes with P = 0.10 as the critical value to enter and excluding ten variables (age, sex, body mass index, hypertension, diabetes, smoking, reason LDL- / HDL, number of affected vessels, statin therapy and beta-blockers) in the model.

Risk ratios (RR) and 95% confidence interval (CI) with bilateral probability values are reported. The assumption of proportional hazards was tested using conventional test-based procedures to determine the significant slope of the smooth curve through the dispersion of rescaled Schoenfeld residual errors over time.

To further assess the predictive capacity of the models, the cardiovascular assessment criterion at two years was considered as a binary variable and logistic regression was performed. Associated receiver operating characteristic (ROC) curves were plotted for predicted probabilities, for a basic model that contains classic risk factors and models that additionally contain PCT, CRP and BNP. The areas under the corresponding curve were calculated together with the 95% CI.

The combined role of PCT and BNP on cardiovascular risk was also evaluated and, therefore, they tested the interaction followed by a dichotomized analysis of both variables using the highest quartile as the cut-off point. The risk ratios and the 95% CI were reported, together with the P values. Another graphic representation of cumulative events was estimated using the Kaplan-Meier procedure for these four subgroups and compared using the logarithmic range test.

Since P values do not fit for multiple tests, they should be considered descriptive. All calculations were performed using SPSS 15.0 for Windows, version 15.0.1 (SPSS Inc., Chicago, Illinois, USA).

Results

The average age of the study population was 61.3  9.5 years, and 80.5% of the patients were men. The patients in this substudy were divided into four groups according to the quartiles of the PCT levels (table 1). No significant differences were found for the distribution of classic risk factors. CRP levels were higher in the fourth quartile than in the others (3.66 mg / l versus 1.81-2.10 mg / l). A moderate correlation was found between PCT and CRP (r = 0.27).

The predictive value of PCT was also evaluated in the subgroup analysis (table 2). The median levels were used to dichotomize continuous variables. In particular, PCT levels were strongly predictive in patients with a serum BNP concentration higher than the median of 37.48 pg / ml with an increased risk of 2.41 times (95% CI 1.32-4, 42; P = 0.004) for the highest PCT quartile.

Table 3 shows the association between PCT, CRP and BNP with future cardiovascular events. The percentage of events increased throughout the quartiles (Figure 1) so that patients in the largest PCT quartile were associated with a 2.27 fold increase (95% CI: 1.14-4.51; P = 0.02) in the risk of future cardiovascular events in a model adjusted for age and sex. This association remained significant in models that fit for most possible confounding factors. If analyzed as a continuous variable, an increase in a standard deviation (SD) of PCT revealed a 1.33 times higher risk (95% CI: 1.02-1.74; P = 0.04) of future cardiovascular events . BNP levels have been independently related to the primary endpoint, while no significant association between CRP and the cardiovascular outcome could be observed in the fully adjusted model.

All classic risk factors and clinical variables were introduced in a retrograde multiple step regression analysis as shown in Table 4. Continuous variables were logarithmically transformed and treated by an increase in a D.E. PCT (RR of 1.30, 95% CI: 1.00-1.70; P = 0.05) was selected as an independent prognostic factor for cardiovascular risk. The final model also revealed the LDL- / HDL ratio, female sex and insulin-dependent diabetes mellitus (DMID) as prognostic factors for the primary endpoint.

In another sub-analysis, a PCT level of 0.05 ng / ml (according to the functional test sensitivity) was chosen as the cut-off value for the prediction of cardiovascular risk. 25% of the 39 patients with PCT levels greater than 0.05 ng / ml experienced cardiovascular events and had a significantly worse prognosis (Figure 2). When introduced in a retrograde multiple step regression analysis, a PCT level greater than 0.05 ng / ml is associated with a 4.22-fold higher cardiovascular risk (95% CI: 2.07-8.59; P <0.001) (table 5).

To further explore whether PCT and BNP added information beyond that obtained from classical risk factors, the area under the ROC curve (AUC) associated with the prediction of different logistic regression models was calculated, considering the valuation criteria cardiovascular at 2 years as a binary variable (table 6). As there were patients with a follow-up of less than 2 years, only 1057 patients were available for this analysis; 46 of them experienced a cardiovascular episode. The basic model that includes classic risk factors such as age, sex, BMI, hypertension, diabetes mellitus, smoking status, LDL- / HDL ratio, number of affected vessels, beta-blocker and statin therapy revealed an AUC of 0, 74 (95% CI: 0.670.81). Table 6 presents analyzes that compare the basic model with models that additionally include either PCT, or BNP, or both, or all of them. The inclusion of an increase in a D.E. PCT improved the prediction value of the basic model, reporting an increase from 0.74 to 0.77. An increase of a D.E. BNP revealed most of the additional information beyond the application of the basic model. The combination of BNP and PCT with the basic model resulted in the higher forecast accuracy of this model. The results were similar during the 1 year and 3 year follow-up (data not shown).

Due to the strong predictive value of PCT in patients with BNP levels above the median (table 2) and the high accuracy of cardiovascular event prognosis when BNP and PCT were combined in a model with classic risk factors (table 6) Finally, it was evaluated to what extent PCT could be added to the BNP forecast value (table 7). When the interaction test was negative (P = 0.78), an additive effect was assumed for both biomarkers. Patients with elevated levels of both biomarkers in the upper quartile were at the highest risk of future cardiovascular events (RR 7.04; 95% CI 3.40-14.57; P <0.001). Figure 3 provides the Kaplan-Meier survival curves according to PCT and BNP levels in combined analysis.

Discussion

In this prospective cohort of patients with CAD documented by angiography, an independent association of PCT with future cardiovascular events has been demonstrated. This association did not change appreciably after the adjustment of most possible confounding factors, indicating that PCT provides important information on cardiovascular prognosis in addition to the classic risk factors and other clinical variables. The combined analysis of PCT and BNP improved the diagnostic accuracy of future cardiovascular events in the present study.

However, only two studies evaluated the possible use of PCT in the CAD framework. Erren et al. (14) found slightly increased levels of PCT only in patients with CAD with additional peripheral arterial disease (PAD) and analyzed PCT as a marker for atherosclerotic loading in a multi-marker approach.

Ilhan et al. (15) found higher levels of PCT (0.40  0.04 ng / ml as opposed to 0.19  0.02 ng / ml in the control group) among patients with CAD who experienced a cardiovascular episode. Here, the inventors demonstrated that PCT is an independent prognostic factor for future cardiovascular events, in particular by adding information on patients with high levels of BNP, indicating a potential for PCT to better stratify high-risk individuals.

Local or systemic inflammation that affects certain types of tissue, also a host response related to trauma (16) (17) (18) (19) (20), and consecutive monocytic activation are therefore a prerequisite for the production of PCT (4). Expression of PCT messenger RNA by cells

Thus, slightly increased levels of PCT could be an epiphenomenon of inflammatory activity within the vascular wall caused by atherosclerosis. However, PCT seems to play a causal role in monocytic activation: as a chemoattractant initially produced only in adherent monocytes, which later recruit parenchymatous cells from the inflamed tissue for additional PCT production. Therefore, PCT could also have an effect on leukocyte migration (4) (10) (11) (14). Other studies have to investigate whether the effect of PCT demonstrated in vitro stimulation on the gene expression of nitric oxide synthesis (12) also has an influence on endothelial dysfunction caused by atherosclerosis.

In the present study, patients with combined elevation of PCT and BNP levels in the upper quartile present a 7.0-fold increased risk for the primary endpoint compared to a 3.2-fold increased risk when BNP alone was elevated. Therefore, PCT significantly improved the AUC of the basic model (classical risk factors) for risk prediction and even obtained additional information in addition to the BNP biomarker.

BNP is an established marker for left ventricular dysfunction. The PCT septicemia marker could also be a marker for low-grade inflammation, particularly for monocyte activation or endothelial dysfunction, as shown in several in vitro studies. The representation of two different pathological mechanisms by BNP and PCT would explain the improvement in the accuracy of prognosis of future cardiovascular events. Additional studies are necessary to evaluate the role of PCT for risk prediction in diseases other than serious bacterial infections and further elucidate its pathophysiological role in the inflammatory cascade.

Since the choice of cut-off points for the analyzes in Table 7 and Figure 3 is directed by the data, the results can only provide clues, but they have to be validated in independent studies.

In conclusion, PCT is independently related to future cardiovascular events in a population of patients with CAD and could add information for risk stratification, particularly in high-risk individuals.

Summary:

Background: Procalcitonin (PCT) is a well established biomarker for the diagnosis and therapeutic monitoring of sepsis. In vitro studies showed that PCT has an effect on monocyte activation and even nitric oxide synthesis. The present prospective study examined the prognosis impact of PCT in patients with established coronary artery disease (CAD) on cardiovascular outcome.

Procedures: In a substudy of the AtheroGene prospective study, in 1,124 patients with stable CAD, the risk of cardiovascular death and non-fatal myocardial infarction (N = 72) have been evaluated over a median follow-up of 3.8 ( maximum 6.8) years according to the initial concentration of PCT.

Results: The adjusted risk ratio for age and sex for patients within the highest quartile of PCT related an increase of 2.27 times (95% confidence interval (CI): 1.14-4.51; P = 0.02) of the relative risk for cardiovascular death and non-fatal myocardial infarction, compared to the first quartile. The adjustment for classic risk factors and clinical variables did not attenuate this relationship. The inclusion of an increase in a standard deviation of PCT improved the prediction value of a basic model (classical risk factors) for the prediction of cardiovascular risk monitored by the area under the curve (AUC) of a higher cardiovascular risk (CI of the %: 3.40-14.57; P <0.001).

Conclusions: The initial concentration of PCT is independently related to future cardiovascular events in patients with stable CAD.

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Table 1

Initial characteristics of the study population according to quartiles

Characteristic

Q1 Q2 Q3 Q4

(N = 254)

(N = 259) (N = 301) (N = 310)

Procalcitonin (ng / ml)

<0.010 0.010-0.013 0.014-0.020 > 0.020

Age (years)

60.6 10.0 60.5 10.0 61.3 9.5 62.5  8.6

Male sex (%)

72.0 80.3 86.0 82.3

Traditional risk factors Body mass index (kg / m2)

27.0 4.0 27.4 3.8 27.9 3.7 28.4  4.1

Hypertension (%)

80.3 75.7 79.1 83.2

Mellitus diabetes

- Dietary treatment (%)

4.3 2.7 3.7 6.1

-Pharmacotherapy (%)

7.5 8.1 10.3 11.3

- Insulin dependent (%)

8.7 5.8 6.6 12.9

Status as a smoker - Never smoked (%) - Ex-smoker (%) - Current smoker (%)

37.8 42.9 19.3 37.8 44.4 17.8 38.2 46.2 15.6 32.3 51.3 16.5

Lipid state

- LDL-cholesterol (mg / dl)

128.0 123.0 118.0 119.0

(98.0-157.8)

(94.0-144.0) (92.5-143.0) (95.8-150.0)

- HDL-cholesterol (mg / dl)

53.0 49.0 47.0 45.0

(43.0-64.0)

(43.0-58.0) (40.5-56.0) (39.0-55.0)

- Triglycerides (mg / dl)

122.0 117.0 129.0 148.0

(93.8-168.0)

(88.0-156.0) (97.0-188.0) (103.0-201.5)

Clinical variables

Multiple vessel disease (%)

74.4 67.2 72.4 72.9

History of myocardial infarction (%)

42.5 44.4 47.5 38.1

Fraction

 from  ejection ventricular 65.0 69.5 66.0 70.0

left (%)

(53.5-73.0) (60.0-77.0) (55.0-74.0) (58.0-78.0)

Medication

Beta-blocker (%)

58.7 62.2 70.8 62.3

Statin (%)

53.1 51.8 61.4 56.8

ACE inhibitor (%)

62.2 55.6 58.8 59.0

Biomarkers

C-reactive protein (mg / l)

1.85 1.81 2.10 3.66

(0.97-3.79)

(0.98-4.18) (1.17-4.02) (1.76-8.15)

Natriuretic peptide type B (pg / ml)

38.97 40.94 37.64 33.97

(13.79-98.87)

(11.86-92.80) (12.02-102.15) (9.88-101.11)

The data presented are the percentage of patients, mean standard deviation, or median and the interquartile range 25º / 75º for asymmetric variables (| asymmetry |> 1). The left ventricular ejection fraction was available for 818 patients and the type B natriuretic peptide for 1032 patients. LDL indicates low density lipoprotein, HDL high density lipoprotein. To convert the values for cholesterol to millimoles per liter, multiply by 0.02586; To convert the values for triglycerides to millimoles per liter, multiply by 0.01129.

Table 2

Risk reasons (95% confidence interval) for the highest quartile versus the other procalcitonin quartiles according to traditional risk factors (adjusted for age and sex)

RR variable (95% CI) P Value Age *  63 years 1.92 (0.98-3.78) 0.06> 63 years 1.56 (0.77-3.13) 0.21

Gender ** Female 1.81 (0.73-4.51) 0.20 Male 1.72 (0.97-3.06) 0.07

Mass index  26.4 kg / m2 2.22 (0.98-5.01) 0.06 body> 26.4 kg / m2 1.54 (0.83-2.83) 0.17

Hypertension No 1.16 (0.38-3.53) 0.80 Yes 1.98 (1.14-3.43) 0.02

Diabetes mellitus No 1.64 (0.88-3.09) 0.12 Yes 1.93 (0.86-4.31) 0.11

Smoking habit No 1.71 (0.74-3.97) 0.21 Active 1.78 (0.98-3.25) 0.06

Ratio LDL- / HDL 1.51 (0.79-2.89) 0.21 2.18 (1.01-4.72) 0.05

Disease No 2.73 (0.99-7.57) 0.05 multiple vessels Yes 1.58 (0.90-2.77) 0.11

C-reactive protein

1.48 (0.55-3.97) 0.44

 2.34 mg / l 1.58 (0.88-2.83) 0.13

> 2.34 mg /

Natriuretic Peptide

1.19 (0.50-2.86) 0.70  37.48 pg / ml type B

2.41 (1.32-4.42) 0.004

> 37.48 pg / ml l The median levels were used to dichotomize continuous variables.

* adjusted only for sex ** adjusted only for age

Table 3

Risk reasons for future cardiovascular events according to the quartiles of procalcitonin, C-reactive protein and natriuretic peptide type B initial

N

Event RR (95% CI) P value RR (95% CI) Value of

lie

adjusted for totally P

(%)

age / sex tight*

Procalcitonin (ng / ml)

Q1

<0.010 254 12 (5%) one one

Q2

0.010-0.013 259 16 (6%) 1.51 (0.71-3.19) 0.29 1.58 (0.74-3.35) 0.24

Q3

0.014-0.020 301 17 (6%) 1.47 (0.70-3.10) 0.31 1.54 (0.73-3.26) 0.26

Q4

> 0.020 310 27 (9%) 2.27 (1.14-4.51) 0.02 2.08 (1.04-4.18) 0.04

By increment

1.41 (1.07-1.86) 0.01 1.33 (1.02-1.74) 0.04

of D.E.

C-reactive protein (mg / l)

Q1

<1.22 282 11 (4%) one one

Q2

1.22-2.34 282 14 (5%) 1.27 (0.57-2.80) 0.56 1.08 (0.48-2.42) 0.86

Q3

2.35-4.95 279 19 (7%) 1.65 (0.78-3.47) 0.19 1.33 (0.62-2.87) 0.46

Q4

> 4.95 281 28 (10%) 2.48 (1.23-5.02) 0.01 1.87 (0.89-3.92) 0.10

By increment

1.42 (1.14-1.76) 0.002 1.33 (1.05-1.67) 0.02

of D.E.

Natriuretic peptide type B (pg / ml)

Q1

<11.97 258 9 (4%) one one

Q2

11.97-37.48 258 8 (3%) 1.04 (0.40-2.71) 0.94 1.08 (0.41-2.83) 0.88

Q3

37.49-99.40 258 15 (6%) 2.03 (0.87-4.71) 0.10 2.11 (0.89-4.99) 0.09

Q4

> 99.40 258 32 (12%) 5.16 (2.34-11.39) <0.001 5.74 (2.51-13.11) <0.001

By increment

1.84 (1.42-2.38) <0.001 1.93 (1.47-2.54) <0.001

of D.E.

* Multivariate risk factor adjustment included age, sex, body mass index, hypertension, diabetes, smoking status, LDL- / HDL ratio, number of affected vessels, statin therapy and beta blockers

Table 4

Final model of a retrograde multiple-step Cox regression analysis for cardiovascular risk prognostic factors

Variable Procalcitonin (increase of 1 D.E.) Reason LDL- / HDL (increase of 1 D.E.) Sex Diabetes mellitus (DMID)

Risk ratio (95% CI) 1.30 (1.00-1.70) 1.32 (1.03-1.70) 0.57 (0.34-0.96) 2.75 (1 , 53-4.92) P value 0.05 0.03 0.03 0.001

Table 5

Final model of a retrograde multiple-step Cox regression analysis for cardiovascular risk prognostic factors

Variable

Risk ratio (95% CI) P value

Procalcitonin

 0.05 ng / ml 4.22 (2.07-8.59) <0.001

Reason LDL- / HDL (increase of 1 D.E.)

1.34 (1.04-1.73) 0.03

Sex

0.60 (0.36-1.00) 0.05

Diabetes Mellitus (DMID)

2.74 (1.54-4.87) 0.001

Table 6

Incremental effects of procalcitonin, C-reactive protein and type B natriuretic peptide (all logarithmically transformed) on the area under the ROC curve in addition to the classical risk factors (basic model) for the prediction of the primary endpoint after 2 years

Model

AUC 95% CI

Basic model

0.74 0.67-0.81

Basic Model + PCT

0.77 0.70-0.84

Basic Model + CRP

0.75 0.67-0.82

Basic Model + BNP

0.79 0.72-0.87

Basic model + PCT + CRP

0.77 0.70-0.84

Basic model + PCT + BNP

0.81 0.74-0.88

Basic model + CRP + BNP

0.80 0.73-0.87

Basic model + PCT + CRP + BNP

0.81 0.74-0.88

Table 7

Risk ratios and 95% confidence interval for future cardiovascular events according to initial levels of procalcitonin and type B natriuretic peptide in combined analysis (fully adjusted *)

N Procalcitonin (quartile Natriuretic peptide type B Risk ratio Higher P value> 0.021 ng / ml) (highest quartile> 99.40 pg / ml) 95% confidence interval

654 --1 212 + -1.13 (0.56-2.29) 0.74 190 - + 2.44 (1.30-4.56) 0.005

68 + + 7.04 (3.40-14.57) <0.001

* Multivariate risk factor adjustment included age, sex, body mass index, hypertension, diabetes, smoking status, LDL- / HDL ratio, number of affected vessels, statin therapy and beta blockers

Claims (6)

one.

 In vitro procedure for risk stratification of patients with stable coronary artery disease, in which the concentration of procalcitonin in the circulation of such patients is determined using a highly sensitive procalcitonin assay, and in which within the range of procalcitonin concentrations in In the normal range typical of healthy individuals, cut-off values are defined that distinguish groups of individual patients with stable coronary artery disease according to their personal cardiac risk, and patients are assigned to one of these risk groups based on their individual concentrations of procalcitonin and wherein said procalcitonin concentrations in the typical normal range of healthy individuals are procalcitonin concentrations of 0.1 ng / ml procalcitonin or less if measured with a sensitive procalcitonin assay as described in the present application.

2.

Method according to claim 1, wherein patients are stratified with respect to their risks regarding the future incidence of cardiovascular events.

3.

Method according to claims 1 to 2, wherein the patients for whom procalcitonin concentrations of 0.05 ng / ml or more are considered as the patients with the highest cardiac risk.

Four.

Method according to any one of claims 1 to 3, wherein together with the determination of procalcitonin at least one additional analyte useful for cardiovascular prognosis is determined, said at least one analyte being selected from the group consisting of natriuretic peptides, adrenomedulin , endothelin, vasopressin, CRP, neopterin, myeloperoxidase, troponin, GDF-15, cystatin-C and its precursors, their prohormones and associated prohormone fragments.

5.

Method according to claim 4, wherein said additional analyte comprises BNP, proBNP and associated proBNP fragments.

6.

Use of the highly sensitive determination of procalcitonin in the circulation of patients at risk of presenting cardiovascular events for prognostic purposes in a procedure according to claims 1 to 5.